首页|New Machine Learning Findings from University of Boras Outlined (Comparing Featu re Engineering Techniques for the Time Period Categorisation of Novels)
New Machine Learning Findings from University of Boras Outlined (Comparing Featu re Engineering Techniques for the Time Period Categorisation of Novels)
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Investigators discuss new findings in Machine Learning. According to news reporting originating from Boras, Sweden, by NewsRx editors, the research stated, "The growing number of literary works bein g produced and published has emphasised the importance of better cataloguing met hods to handle the increasing volume effectively. One specific issue is the lack of organising works by time periods, which is crucial for understanding and org anising literature." Our news editors obtained a quote from the research from the University of Boras , "In this study, ‘time' refers to when the story's events occur or the narrativ e's temporal setting, like specific historical periods or events, rather than th e publication date. Categorising literary works based on their historical settin gs can significantly improve accessibility for library patrons navigating online catalogues. However, time period categorisation is uncommon, primarily due to t he resource-intensive nature of the process, which necessitates extensive analys is by librarians and cataloguers. To address this issue, this paper proposes eva luating different machine learning workflows to predict time periods for novels. The workflow comprises preprocessing, feature engineering, classification, and evaluation. The feature engineering techniques used are Latent Dirichlet Allocat ion (LDA), Word Embedding with Sentence-BERT (WE SBERT), and Term Frequency-Inve rse Document Frequency (TFIDF), and the classification algorithm used is Logisti c Regression. The models are assessed using the F1 score, precision, and recall metrics. The time period categories used are Medieval, Era of Great Power, Age o f Liberty, and Gustavian periods. The objective is to determine how effectively each model categorises Swedish historical fiction novels into their appropriate time period categories."
BorasSwedenEuropeCyborgsEmerging TechnologiesEngineeringMachine LearningUniversity of Boras
NETL
NSTL
